scholarly journals Principal component analysis for clustering gene expression data

2001 ◽  
Vol 17 (9) ◽  
pp. 763-774 ◽  
Author(s):  
K. Y. Yeung ◽  
W. L. Ruzzo
Keyword(s):  
Gene Expression ◽  
Principal Component Analysis ◽  
Gene Expression Data ◽  
Principal Component ◽  
Component Analysis ◽  
Expression Data

scholarly journals Gene Expression Data Classification With Kernel Principal Component Analysis

2005 ◽  
Vol 2005 (2) ◽  
pp. 155-159 ◽  
Author(s):  
Zhenqiu Liu ◽  
Dechang Chen ◽  
Halima Bensmail
Keyword(s):  
Gene Expression ◽  
Principal Component Analysis ◽  
Gene Expression Data ◽  
Human Tumor ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Support Vector ◽  
Expression Data ◽  
Nonlinear Version

One important feature of the gene expression data is that the number of genesMfar exceeds the number of samplesN. Standard statistical methods do not work well whenN<M. Development of new methodologies or modification of existing methodologies is needed for the analysis of the microarray data. In this paper, we propose a novel analysis procedure for classifying the gene expression data. This procedure involves dimension reduction using kernel principal component analysis (KPCA) and classification with logistic regression (discrimination). KPCA is a generalization and nonlinear version of principal component analysis. The proposed algorithm was applied to five different gene expression datasets involving human tumor samples. Comparison with other popular classification methods such as support vector machines and neural networks shows that our algorithm is very promising in classifying gene expression data.

Rejoinder to Use of Principal Component Analysis and the GE -Biplotfor the Graphical Exploration of Gene Expression Data

Biometrics ◽  
2005 ◽  
Vol 61 (2) ◽  
pp. 632-634
Author(s):  
Luc Wouters ◽  
Hinrich W. Göhlmann ◽  
Luc Bijnens ◽  
Stefan U. Kass ◽  
Geert Molenberghs ◽  
...  
Keyword(s):  
Gene Expression ◽  
Principal Component Analysis ◽  
Gene Expression Data ◽  
Principal Component ◽  
Component Analysis ◽  
Expression Data

scholarly journals Integrative sparse principal component analysis of gene expression data

2017 ◽  
Vol 41 (8) ◽  
pp. 844-865 ◽  
Author(s):  
Mengque Liu ◽  
Xinyan Fan ◽  
Kuangnan Fang ◽  
Qingzhao Zhang ◽  
Shuangge Ma
Keyword(s):  
Gene Expression ◽  
Principal Component Analysis ◽  
Gene Expression Data ◽  
Principal Component ◽  
Component Analysis ◽  
Expression Data ◽  
Sparse Principal Component Analysis

CLUSTERING GENE EXPRESSION DATA WITH KERNEL PRINCIPAL COMPONENTS

2005 ◽  
Vol 03 (02) ◽  
pp. 303-316 ◽  
Author(s):  
ZHENQIU LIU ◽  
DECHANG CHEN ◽  
HALIMA BENSMAIL ◽  
YING XU
Keyword(s):  
Gene Expression ◽  
Principal Component Analysis ◽  
Gene Expression Data ◽  
Microarray Data ◽  
Principal Components ◽  
Data Clustering ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
Expression Data ◽  
Fuzzy C Means

Kernel principal component analysis (KPCA) has been applied to data clustering and graphic cut in the last couple of years. This paper discusses the application of KPCA to microarray data clustering. A new algorithm based on KPCA and fuzzy C-means is proposed. Experiments with microarray data show that the proposed algorithms is in general superior to traditional algorithms.

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